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← Domain knowledge can be more important than data when investigating cause-and-effect using AI.
17 connected korrents · 17 moments from 1 Jan 2020 to 29 Oct 2026.
Everything filed under AI and science
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humility Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Domain knowledge can be more important than data when investigating cause-and-effect using AI. Domain knowledge can be more important than data when investigating cause-and-effect using AI. Last stated 2 months ago
12 Sept 2026
AK
Adam Kucharski — holds since 2026-09-12 — tap for who they are
Same subject: When working with AI, intent and being able to play with the outcome matter more than knowing the technical language. — tap to centre the map on it When working with AI, intent and being able to play with the outcome matter more than knowing the technical language. Last stated 8 months ago
12 Mar 2026
JP
Josh Puckett — holds since 2026-03-12 — tap for who they are
Same subject: The surrounding system framework and scaffolding matter more for AI agent capability than the underlying model. — tap to centre the map on it The surrounding system framework and scaffolding matter more for AI agent capability than the underlying model. Last stated 5 months ago
21 Jun 2026
EY
Eugene Yan — holds since 2026-06-21 — tap for who they are
Same subject: AI struggles more with general knowledge work than with coding because that work is more fragmented and less verifiable. — tap to centre the map on it AI struggles more with general knowledge work than with coding because that work is more fragmented and less verifiable. Last stated 10 months ago
22 Dec 2025
IZ
Ivan Zhao — holds since 2025-12-22 — tap for who they are
Same subject: AI errors are harder to catch in analysis of unfamiliar data than in simple writing tasks. — tap to centre the map on it AI errors are harder to catch in analysis of unfamiliar data than in simple writing tasks. Last stated a year ago
5 Sept 2025
JE
Jason Evanish — holds since 2025-09-05 — tap for who they are
Same subject: Process knowledge — the experience of actually making things — matters more than tools or blueprints. — tap to centre the map on it Process knowledge — the experience of actually making things — matters more than tools or blueprints. Last stated 7 years ago
1 Jan 2020
DW
Dan Wang — holds since 2020-01-01 — tap for who they are
Same subject: Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson. — tap to centre the map on it Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson. Last stated 8 months ago
11 Mar 2026
SY
Steve Yegge — holds since 2026-03-11 — tap for who they are
Same subject: In no small part thanks to AI, more companies than ever understand the need for outcomes and not just output. — tap to centre the map on it In no small part thanks to AI, more companies than ever understand the need for outcomes and not just output. Last stated 2 months ago
10 Sept 2026
MC
Marty Cagan — holds since 2026-09-10 — tap for who they are
Same subject: Accepting an AI's polished explanation of the data without examining it only makes it easier to sound as if the problem is solved. — tap to centre the map on it Accepting an AI's polished explanation of the data without examining it only makes it easier to sound as if the problem is solved. Last stated a week ago
29 Oct 2026
RM
Ravi Mehta — holds since 2026-10-29 — tap for who they are
Same subject: A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. — tap to centre the map on it A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. Last stated 2 years ago
13 May 2024
DL
Derek Lowe — holds since 2024-05-13 — tap for who they are
Same subject: A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. — tap to centre the map on it A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. Last stated 2 years ago
17 Sept 2024
AT
Alex Telford — holds since 2024-09-17 — tap for who they are
Same subject: A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. — tap to centre the map on it A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. — tap to centre the map on it Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. — tap to centre the map on it AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. Last stated 11 months ago
23 Nov 2025
EH
Elliot Hershberg — holds since 2025-11-23 — tap for who they are
Same subject: AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work. — tap to centre the map on it AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work. Last stated 2 years ago
19 Sept 2024
DL
Derek Lowe — holds since 2024-09-19 — tap for who they are
Same subject: AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. — tap to centre the map on it AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape. — tap to centre the map on it AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape. Last stated 8 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: AI has solved in an afternoon of compute maths problems that stumped the best human minds for decades. — tap to centre the map on it AI has solved in an afternoon of compute maths problems that stumped the best human minds for decades. Last stated 4 weeks ago
7 Oct 2026
AA
Azeem Azhar — holds since 2026-10-07 — tap for who they are
At the centre
Domain knowledge can be more important than data when investigating cause-and-effect using AI.
Holds Read this korrent →
When working with AI, intent and being able to play with the outcome matter more than knowing the technical language.
Holds
The surrounding system framework and scaffolding matter more for AI agent capability than the underlying model.
Holds
AI struggles more with general knowledge work than with coding because that work is more fragmented and less verifiable.
Holds
AI errors are harder to catch in analysis of unfamiliar data than in simple writing tasks.
Holds
Process knowledge — the experience of actually making things — matters more than tools or blueprints.
Holds
Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson.
Holds
In no small part thanks to AI, more companies than ever understand the need for outcomes and not just output.
Holds
Accepting an AI's polished explanation of the data without examining it only makes it easier to sound as if the problem is solved.
Holds
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
Holds
A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck.
Holds
A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.
Holds
Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics.
Holds
AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points.
Holds
AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work.
Holds
AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder.
Holds
AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape.
Holds
AI has solved in an afternoon of compute maths problems that stumped the best human minds for decades.
Holds